This is the manuscript that was published, with only minor copy-editing alterations, as: Haidt, J. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral judgment. Psychological Review. 108, 814-834 Copyright 2001, American Psychological Association To obtain a reprint of the final type-set article, please go through your library’s journal services, or contact the author directly Research on moral judgment has been dominated by rationalist models, in which moral judgment is thought to be caused by moral reasoning. Four reasons are given for considering the hypothesis that moral reasoning does not cause moral judgment; rather, moral reasoning is usually a post-hoc construction, generated after a judgment has been reached. The social intuitionist model is presented as an alternative to rationalist models. The model is a social model in that it de-emphasizes the private reasoning done by individuals, emphasizing instead the importance of social and cultural influences. The model is an intuitionist model in that it states that moral judgment is generally the result of quick, automatic evaluations (intuitions). The model is more consistent than rationalist models with recent findings in social, cultural, evolutionary, and biological psychology, as well as anthropology and primatology. Author notes

Early studies of intuitive judgment and decision making conducted with the late Amos Tversky are reviewed in the context of two related concepts: an analysis of accessibility, the ease with which thoughts come to mind; a distinction between effortless intuition and deliberate reasoning. Intuitive thoughts, like percepts, are highly accessible. Determinants and consequences of accessibility help explain the central results of prospect theory, framing effects, the heuristic process of attribute substitution, and the characteristic biases that result from the substitution of nonextensional for extensional attributes. Variations in the accessibility of rules explain the occasional corrections of intuitive judgments. The study of biases is compatible with a view of intuitive thinking and decision making as generally skilled and successful.

Construal level theory proposes that temporal distance changes people’s responses to future events by changing the way people mentally represent those events. The greater the temporal distance, the more likely are events to be represented in terms of a few abstract features that convey the perceived essence of the events (high-level construals) rather than in terms of more concrete and incidental details of the events (low-level construals). The informational and evaluative implications of high-level construals, compared with those of low-level construals, should therefore have more impact on responses to distant-future events than near-future events. This article explores the implications of construal level theory for temporal changes in evaluation, prediction, and choice. The authors suggest that construal level underlies a broad range of evaluative and behavioral consequences of psychological distance from events.

Automation is often problematic because people fail to rely upon it appropriately. Because people respond to technology socially, trust influences reliance on automation. In particular, trust guides reliance when complexity and unanticipated situations make a complete understanding of the automation impractical. This review considers trust from the organizational, sociological, interpersonal, psychological, and neurological perspectives. It considers how the context, automation characteristics, and cognitive processes affect the appropriateness of trust. The context in which the automation is used influences automation performance and provides a goal-oriented perspective to assess automation characteristics along a dimension of attributional abstraction. These characteristics can influence trust through analytic, analogical, and affective processes. The challenges of extrapolating the concept of trust in people to trust in automation are discussed. A conceptual model integrates research regarding trust in automation and describes the dynamics of trust, the role of context, and the influence of display characteristics. Actual or potential applications of this research include improved designs of systems that require people to manage imperfect automation.

Who knows what I want to do? Who knows what anyone wants to do? How can you be sure about something like that? Isn't it all a question of brain chemistry, signals going back and forth, electrical energy in the cortex? How do you know whether something is really what you want to do or just some kind of nerve impulse in the brain. Some minor little activity takes place somewhere in this unimportant place in one of the brain hemispheres and suddenly I want to go to Montana or I don't want to go to Montana. (White Noise, Don DeLillo)

It must be troubling for the god who loves you To ponder how much happier you'd be today Had you been able to glimpse your many futures. —Dennis (2001, p. 72) Foreseeing the future is one of the most appealing of all psychic powers. Who has not dreamed of making millions by predicting which new offering on Wall Street will be the next Microsoft and whether the Red Sox or Phillies will win the World Series? Seeing into the future would bring many advantages other than fattening our wallets, such as eliminating all decisionmaking angst. Rather than worrying about whether we are best suited for a career as a lawyer or an interior designer, whether we should marry Sam or Harry, or whether we should buy our neighbor's 1992 Volvo, we could simply glance into our crystal balls and see how these various options would pan out. People do not have crystal balls, of course (at least not accurate ones) and thus must prognosticate as best they can, based on what they know in the present. There is a great deal of research on how people make predictions about the future, including decision making under uncertainty (e.g.,

Modern theories in cognitive psychology and neuroscience indicate that there are two fundamental ways in which human beings comprehend risk. The “analytic system ” uses algorithms and normative rules, such as the probability calculus, formal logic, and risk assessment. It is relatively slow, effortful, and requires conscious control. The “experiential system ” is intuitive, fast, mostly automatic, and not very accessible to conscious awareness. The experiential system enabled human beings to survive during their long period of evolution and remains today the most natural and most common way to respond to risk. It relies on images and associations, linked by experience to emotion and affect (a feeling that something is good or bad). This system represents risk as a feeling that tells us whether it’s safe to walk down this dark street or drink this strange-smelling water. Proponents of formal risk analysis tend to view affective responses to risk as irrational. Current wisdom disputes this view. The rational and the experiential systems operate in parallel and each seems to depend on the other for guidance. Studies have demonstrated that analytic reasoning cannot be effective unless it is guided by emotion and affect. Rational decision making requires proper integration of both modes of

communication between experts and the public:

Bernoulli’s framework of expected utility serves as a model for various psychological processes, including motivation, moral sense, attitudes, and decision making. To account for evidence at variance with expected utility, the authors generalize the framework of fast and frugal heuristics from inferences to preferences. The priority heuristic predicts (a) the Allais paradox, (b) risk aversion for gains if probabilities are high, (c) risk seeking for gains if probabilities are low (e.g., lottery tickets), (d) risk aversion for losses if probabilities are low (e.g., buying insurance), (e) risk seeking for losses if probabilities are high, (f) the certainty effect, (g) the possibility effect, and (h) intransitivities. The authors test how accurately the heuristic predicts people’s choices, compared with previously proposed heuristics and 3 modifications of expected utility theory: security-potential/aspiration theory, transfer-of-attentionexchange model, and cumulative prospect theory.

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